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Two Elementary Formulae and Some Complicated Properties for Mertens Function3 TWO ELEMENTARY FORMULAE AND SOME COMPLICATED PROPERTIES FOR MERTENS FUNCTION RONG QIANG WEI Abstract. Two elementary formulae for Mertens function M(n) are obtained. With these formulae, M(n) can be calculated directly and simply, which can be easily imple- mented by computer. M(1) ∼ M(2 × 107) are calculated one by one. Based on these 2 × 107 samples, some of the complicated properties for Mertens function M(n), its 16479 zeros, the 10043 local maximum/minimum between two neighbor zeros, and the rela- tion with the cumulative sum of the squarefree integers, are understood numerically and empirically. Keywords: Mertens function Elementary formula Zeros Local maximum/minimum squarefree integers 1. Introduction The Mertens function M(n) is important in number theory, especially the calculation of this function when its argument is large. M(n) is defined as the cumulative sum of the M¨obius function µ(k) for all positive integers n, n (1) M(n)= µ(k) Xk=1 where the M¨obius function µ(k) is defined for a positive integer k by 1 k = 1 (2) µ(k)= 0 if k is divisible by a prime square ( 1)m if k is the product of m distinct primes − Sometimes the above definition can be extended to real numbers as follows: arXiv:1612.01394v2 [math.NT] 15 Dec 2016 (3) M(x)= µ(k) 1≤Xk≤x Furthermore, Mertens function has other representations. They are mainly shown in formula (4-7): 1 c+i∞ xs (4) M(x)= ds 2πi Zc−i∞ sζ(s) 1 2 RONG QIANG WEI where ζ(s) is the the Riemann zeta function, s is complex, and c> 1 (5) M(n)= exp(2πia) aX∈Fn where Fn is the Farey sequence of order n. n n n n n n n n n a≤n a≤ b b≤ a a≤ bc b≤ ac b≤ ab a≤ bcd b≤ acd b≤ abd b≤ abc (6) M(n) = 1 1+ 1 1+ 1 . − − Xa=2 Xa=2 Xb=2 Xa=2 Xb=2 Xc=2 Xa=2 Xb=2 Xc=2 Xd=2 Formula (6) shows M(n) is the sum of the number of points under n-dimensional hyper- boloids. (7) M(n) = detRn×n where R is the Redheffer matrix. R = r is defined by r = 1 if j = 1 or i j, and { ij} ij | rij = 0 otherwise. However, none of the representations above results in practical algorithms for calculating the Mertens function. At present, many algorithms are based or partially based on sieving similar to those used in prime counting (eg., Kotnik and van de Lune, 2004; Kuznetsov, 2011; Hurst, 2016). With the sieve algorithm, M(x) has been computed for all x 1022(Kuznetsov, 2011). For the isolated values of Mertens function M(n), M(2n) has been≤ computed for all positive integers n 73 with combinatorial algorithm (partially sieving) (Hurst, 2016). ≤ On the other hand, there are also some (recursive) formulae for Mertens function M(n) in the mathematical literature by which the practical algorithms for the M(n) can be obtained (eg., Neubauer, 1963; Dress, 1993; Benito and Varona 2008; and the references therein). For example, Benito and Varona (2008) present a two-parametric family of recursive formula as follows, n r+1 2M(n)+3 = ⌊ ⌋ g(n, k)µ(k) k=1 sP (8) n n n n + [M( 3+6b ) 2M( 5+6b ) + 3M( 6+6b ) 2M( 7+6b )] b=0 − − Pr + h(a)[M( n ) M( n )] a − a+1 a=6Ps+9 where n, r, and s are three integers such that s 0 and 6s + 9 r n 1. g(n, k) = 3 n 2 n 1 . h(a)= g(n, k) for n < k n .≥ ≤ ≤ − ⌊ 3k ⌋− ⌊ 2k − 2 ⌋ a+1 ≤ a Here we introduce two new elementary formulae to calculate the M(n) which are not based on sieving. We calculate Mertens function from M(1) to M(2 107) with these formulae one by one, and study some properties of the M(n) numerically× and empirically. TWO ELEMENTARY FORMULAE AND SOME COMPLICATED PROPERTIES FOR MERTENS FUNCTION3 2. Two elementary formulae for Mertens function In Wei (2016), a definite recursive relation for M¨obius function is introduced by two simple ways. One is from M¨obius transform, and the other is from the submatrix of the Redheffer Matrix. The recursive relation for M¨obius function µ(k) is, k−1 1 m k (9) µ(k)= lkmµ(m), k = 2, 3, ; lkm = | − · · · 0 else mX=1 and µ(1) = 1. From formula (9), two elementary formulae for Mertens function M(n) can be obtained as follows, n n k−1 1 m k (10) M(n)= µ(k)=1+ [ lkmµ(m)], k = 2, 3, ; lkm = | − · · · 0 else Xk=1 Xk=2 mX=1 n−1 1 m n (11) M(n)= M(n 1) lnmµ(m),n = 2, 3, ; lnm = | − − · · · 0 else mX=1 where M(1)=1. With formula (10) or (11), M(n) can be calculated directly and the most complex operation is only the Mod. We calculated the Mertens function from M(1) to M(2 107) one by one. Some values of the M(n) are listed in Table 1. × Table 1: Some values of the Mertens function M(n) n M(n) n M(n) n M(n) n M(n) n M(n) n M(n) 10 -1 20 -3 30 -3 40 0 50 -3 60 -1 102 1 2 102 -8 3 102 -5 4 102 1 5 102 -6 6 102 4 103 2 2 × 103 5 3 × 103 -6 4 × 103 -9 5 × 103 2 6 × 103 0 104 -23 2 × 104 26 3 × 104 18 4 × 104 -10 5 × 104 23 6 × 104 -83 105 -48 2 × 105 -1 3 × 105 220 4 × 105 11 5 × 105 -6 6 × 105 -230 106 212 2 × 106 -247 3 × 106 107 4 × 106 192 5 × 106 -709 6 × 106 257 107 1037 2 × 107 -953 3.5× 106 -138 4.5× 106 173 5.5× 106 -513 6.5× 106 867 × × × × × 3. Some complicated properties for Mertens function M(n) 3.1. Variation of Mertens function M(n) with n. Figure 1 shows the Mertens function M(n) to n = 5 105, n = 1 106, n = 1.5 107, and n = 2 107, respectively. It can be found the distribution× of Mertens× function M× (n) is complicated.× It oscillates up and down 4 RONG QIANG WEI with increasing amplitude over n, but grows slowly in the positive or negative directions until it increases to a certain peak value. n=5000000 n=10000000 800 1500 600 1000 400 500 200 ) ) n n ( 0 ( 0 M -200 M -500 -400 -1000 -600 -800 -1500 0 1 2 3 4 5 0 2 4 6 8 10 n ×106 n ×106 n=15000000 n=20000000 1500 1500 1000 1000 500 500 ) ) n n ( 0 ( 0 M M -500 -500 -1000 -1000 -1500 -1500 0 5 10 15 0 0.5 1 1.5 2 n ×106 n ×107 Fig. 1: Mertens function M(n) to n = 5 105, 1 106, 1.5 107, 2 107. × × × × To investigate further the complicated properties of the M(n), a sequence composed of M(1) M(5 105) is analyzed by the method of empirical mode decomposition (EMD) (Tan,− 2016). This× sequence is decomposed into a sum of 19 empirical modes. Some models are shown in Figure 2-3. It can be seen that the empirical modes are still complicated except 16th-19th modes. The fundamental mode (the last mode in Figure 3) is a simple parabola going downward. These empirical modes show further that the Mertens function M(n) has complicated behaviors. It can be found that there is a different variation of the Mertens function M(n) with n in a log-log space. Figure 4 shows the absolute value of the Mertens function M(n) to n = 2 107 in such a space. It can be found M(n) still oscillates but increases| generally| with n×. It seems that the outermost values of| M(n|) increase almost linearly with n, but they are less than those from √n. | | It seems from Figure 1 and 4 to the M(n) has some fractal properties. We check this by estimating M(n)’s power spectral density (PSD). We calculate the PSD for M(n) sequence from M(1) to M(2 107) by taking n as time. The result is shown in Figure 5. It can be found that the logarithmic× PSD of M(n) has a linear decreasing trend with increasing logarithmic frequency, which does indicate that M(n) has some fractal properties. However, the M¨obius function µ(n), which is taken as an independent random sequence in Wei (2016), has no such properties. Its cumulative sum, M(n) reduces partly the randomness. TWO ELEMENTARY FORMULAE AND SOME COMPLICATED PROPERTIES FOR MERTENS FUNCTION5 20 10 0 -10 -20 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 ×105 40 20 0 -20 -40 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 ×105 50 0 -50 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 ×105 100 50 0 -50 -100 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 n ×105 Fig. 2: The 9th-12th (from the top to the bottom) empirical modes of the sequence com- posed of M(1) M(5 105) − × 10 0 -10 -20 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 ×105 20 0 -20 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 ×105 15 10 5 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 n ×105 Fig.
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